Analisis Perbandingan Data Sintesis Privacy-Preserving Menggunakan CTGAN dan Metode Differential Privacy untuk Pelatihan Model LSTM dalam Klasifikasi Deteksi Jatuh pada Lansia
Abstract
Falls among the elderly are a leading cause of fatal injuries and deaths worldwide. The significant growth of the elderly population has led to a high prevalence of falls and driven the need for early fall detection solutions for the elderly. The development of Fall Detection Systems (FDS) offers a solution, as FDS can provide a rapid response when a fall occurs. However, the development of FDS faces challenges such as data limitations, class imbalance, and privacy violation risks. This study aims to compare two privacy-preserving methods: the CTGAN model and DP. The research begins with the implementation of an Internet of Things (IoT)-based FDS and the collection of elderly activity data. Next, statistical utility was measured using the Kolmogorov–Smirnov (KS) complement and Wasserstein distance, privacy aspects were assessed using the Nearest Neighbor Distance Ratio (NNDR), and model utility was evaluated using the Train-on-Synthetic, Test-on-Real (TSTR) scenario. The results show that CTGAN is a better privacy- preserving method than DP; in terms of statistical utility, CTGAN outperforms DP with a KS complement value of 0.902 and a Wasserstein distance of 0.329, compared to DP’s 0.861 and 0.690. In terms of privacy, CTGAN outperforms DP at the 5th percentile with a value of 0.63 compared to 0.57 for DP. Finally, in terms of model utility, CTGAN outperforms DP with higher accuracy and F1-score values than DP. Keywords: conditional tabular generative adversarial network, differential privacy, fall detection, long short-term memory, privacy-preserving
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References
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